Tag: HAQM S3

Predicting demand for medical products can be a formidable challenge, since many items have no underlying seasonality patterns nor a consistent shelf life. Learn how Ganit worked with a client to achieve reductions in inventory by designing a robust solution with HAQM Forecast. This post details the approach used to define the objectives and discover the data treatments, and cover employing the flexible architecture provided by Forecast to turn the client’s data into a strength.

Federated learning (FL) addresses the need of preserving privacy while having access to large datasets for machine learning model training. The NVIDIA FLARE (which stands for Federated Learning Application Runtime Environment) platform provides an open-source Python SDK for collaborative computation and offers privacy-preserving FL workflows at scale. NVIDIA is an AWS Competency Partner that has pioneered accelerated computing to tackle challenges in AI and computer graphics.

Take an in-depth view of the cloud migration and data platform modernization process for Flipboard, which engaged Mactores Cognition for a thorough assessment of the self-managed platform and help migrating existing data workloads to a fully managed HAQM EMR serverless big data platform. The process streamlined Flipboard’s distributed database capabilities, allowing the social media platform to support user spikes at scale, maximize throughput performance, and prepare to expand the user base exponentially.

Infusing machine learning into core business processes such as credit scoring creates a competitive edge for banks and financial services institutions. It does not require a data science team, expertise, or platform rollout. Explore an ML-based credit-decisioning model built by ElectrifAi in collaboration with AWS whose model rapidly determines the creditworthiness of a SME, and data-driven, actionable insights reduce the overall processing cost and are consistent and free from any potential human biases.

Enterprises require the ability to be proactive on modern governance challenges. The difficulty is knowing what data you have, where it’s located, its business value or risk to the organization, and how it can be protected. The Smarsh Enterprise Platform enables companies to capture, retain, analyze, and act on the “signals” in communications that are most critical to the business. These include compliance and brand risks and may expand to include security threats, cultural indicators, untapped revenue opportunities, and more.

DevSecOps teams are responsible for providing enhanced infrastructure observability while ensuring they have the ability to respond to security events in a matter of minutes across the entire organization. To address this challenge, Sumo Logic and AWS collaborated to build a solution that provides end-to-end security and incident management (SIEM) across an enterprise using AWS Organizations. This SIEM solution is based on the AWS Security Reference Architecture.

Featurization is one of the most difficult problems in machine learning. Learn how graph features engineered in Neo4j can be used in a supervised learning model trained with HAQM SageMaker. These novel graph features can improve model performance beyond what’s possible with more traditional approaches. Together, these components offer a graph platform that can be used to understand graph data and operationalize graph use cases.

Digital food ordering is one of the most rapidly growing global industries today. Cognizant’s OrderServ 2.0 platform is an omni-channel digital ordering platform designed for the restaurant and food services industries. It has built-in connectors for seamless integration with restaurant point-of-sale (POS) systems, master data management, payments services, loyalty programs, and other business applications. OrderServ 2.0 is offered as a SaaS platform hosted on AWS.

A digital journey is of strategic importance for many organizations, and digital transformation enabled by cloud technologies has increased efficiency and raised productivity with improved stakeholder experiences. To achieve these outcomes, transformation initiatives need to be holistic, interlinked, and inclusive. Learn how to supercharge customer experiences and make your contact center future-ready by leveraging HCLTech’s Fluid Contact Center Intelligence (Fluid CCI) and AWS AI/ML services.

This post explores an asynchronous pattern for ingesting data from legacy systems, collecting it into projections, and aggregating it into single views. The purpose of this solution is to decouple the source systems where data is stored from the external channels that request data—ensuring both offloading of source systems and making data available to channels 24/7 and in near real-time. The proposed solution is a simplification of the high-end architecture of Mia-Platform Fast Data.